The safe operation of battery energy storage systems (BESSs) has become one of the research priorities in this industry. And it is usually threated by various faults caused by design flaws, environmental conditions, and operating conditions et al. Among these faults, the internal short circuit (ISC) faults pose a significant threat to the safety of BESSs. Relevant studies focus on ISC fault diagnosis itself and ignore the impact of battery aging within the pack on fault diagnosis. To solve this problem, this paper proposes an ISC fault diagnosis method based on incremental capacity (IC) curves. And a qualitative differentiation between ISC batteries and aging ones is first achieved by leveraging the characteristic variations of IC curves. Then, an equivalent circuit model is constructed for ISC batteries. Further, a joint estimation of ISC resistance and SOC of the faulty battery is performed by combining Extended Kalman Filtering (EKF) and Forgetting Factor Recursive Least Squares (FFRLS). Finally, an experimental platform is established to verify the proposed method. Results show the proposed method can effectively differentiate between ISC batteries and aging batteries. Moreover, the estimation errors of SOC are less than 0.26% and the estimation accuracy of ISC resistance is more than 99.42%.

中文翻译：

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基于增量容量曲线的电池内部短路故障诊断方法

电池储能系统（BESS）的安全运行已成为该行业的研究重点之一。并且常常受到因设计缺陷、环境条件、运行条件等引起的各种故障的威胁。其中，内部短路（ISC）故障对BESS的安全构成重大威胁。相关研究主要关注ISC故障诊断本身，而忽略了PACK内电池老化对故障诊断的影响。针对这一问题，提出一种基于增量容量（IC）曲线的ISC故障诊断方法。首先通过利用 IC 曲线的特征变化来实现 ISC 电池和老化电池之间的定性区分。然后，构建了ISC电池的等效电路模型。进一步，结合扩展卡尔曼滤波（EKF）和遗忘因子递归最小二乘法（FFRLS）对故障电池的ISC内阻和SOC进行联合估计。最后搭建实验平台对所提方法进行验证。结果表明，该方法可以有效地区分ISC电池和老化电池。此外，SOC估计误差小于0.26%，ISC电阻估计精度大于99.42%。